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preservation of historic infrastructure, we welcome your application. Job requirements You excel in developing complex numerical models and analysing simulations, bringing strong communication skills
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the mechanical behavior of these materials at the nanoscale. Subsequently, a molecular dynamics model will be developed to simulate the matrix–nanotube interaction, analyzing the effects of adhesion, orientation
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 28 days ago
dynamics simulations of biomolecules in solution – using models of about 100 000 atoms – that provide a data set to train stochastic low-dimensional models (Langevin equations), based on diffusion on a
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, and procedures related to campus safety. Go here to review: https://safety.temple.edu/reports-logs/annual-security-report You may request a copy of the report by calling Temple Department of Public
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changes in microgravity conditions. d. Ability to perform cell culture and tissue models such as 3D tissue models and microfluidic systems to simulate microgravity. e. Understanding and experience with
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Center for Astrophysics | Harvard & Smithsonian | Cambridge, Massachusetts | United States | 1 day ago
world-class scientists, contribute to current and on-going research, and develop essential skills in data analysis, numerical modeling, and scientific communication. With personalized mentorship, access
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. The ideal candidate will have a strong background in modeling and simulation of atomic and molecular structures, along with extensive experience in analyzing the electronic and structural properties
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energy consumption. The demonstration of high-power energy recovery (exceeding 1 MW), particularly for high currents, remains a major challenge. It requires detailed simulations of beam dynamics o study
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The Doctoral Candidate will: Perform numerical modelling of the three NDE techniques to evaluate the influence of relevant material property gradients on each NDE observable generating a sizable synthetic
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the following is desired: - Agentic and sequential decision-making for autonomous experimentation, including active learning and optimal experimental design - Generative and probabilistic modeling